Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?

Fort, Hugo

Resumen:

A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out.


Detalles Bibliográficos
2018
Biodiversity–ecosystem functioning experiments
Quantitative Lotka-Volterra competition theory
Inglés
Universidad de la República
COLIBRI
https://hdl.handle.net/20.500.12008/31683
Acceso abierto
Licencia Creative Commons Atribución (CC - By 4.0)
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author Fort, Hugo
author_facet Fort, Hugo
author_role author
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collection COLIBRI
dc.contributor.filiacion.none.fl_str_mv Fort Hugo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.
dc.creator.none.fl_str_mv Fort, Hugo
dc.date.accessioned.none.fl_str_mv 2022-05-27T12:46:13Z
dc.date.available.none.fl_str_mv 2022-05-27T12:46:13Z
dc.date.issued.none.fl_str_mv 2018
dc.description.abstract.none.fl_txt_mv A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out.
dc.format.extent.es.fl_str_mv 4 h
dc.format.mimetype.es.fl_str_mv application/pdf
dc.identifier.citation.es.fl_str_mv Fort, H. "Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?". Community Ecology. [en línea] 2018, 19: 199–202. 4 h.
dc.identifier.doi.none.fl_str_mv 10.1556/168.2018.19.2.12
dc.identifier.issn.none.fl_str_mv 1585-8553
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12008/31683
dc.language.iso.none.fl_str_mv en
eng
dc.publisher.es.fl_str_mv Akadémiai Kiadó, Budapest
dc.relation.ispartof.es.fl_str_mv Community Ecology, 2018, 19: 199–202
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.subject.en.fl_str_mv Biodiversity–ecosystem functioning experiments
Quantitative Lotka-Volterra competition theory
dc.title.none.fl_str_mv Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
description A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out.
eu_rights_str_mv openAccess
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identifier_str_mv Fort, H. "Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?". Community Ecology. [en línea] 2018, 19: 199–202. 4 h.
1585-8553
10.1556/168.2018.19.2.12
instacron_str Universidad de la República
institution Universidad de la República
instname_str Universidad de la República
language eng
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publishDate 2018
reponame_str COLIBRI
repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository_id_str 4771
rights_invalid_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
spelling Fort Hugo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.2022-05-27T12:46:13Z2022-05-27T12:46:13Z2018Fort, H. "Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?". Community Ecology. [en línea] 2018, 19: 199–202. 4 h.1585-8553https://hdl.handle.net/20.500.12008/3168310.1556/168.2018.19.2.12A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out.Submitted by Faget Cecilia (lfaget@fcien.edu.uy) on 2022-05-27T12:37:30Z No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 10.1556168.2018.19.2.12.pdf: 1067858 bytes, checksum: 67581f069ade551a17298bce97d3a72e (MD5)Approved for entry into archive by Faget Cecilia (lfaget@fcien.edu.uy) on 2022-05-27T12:38:00Z (GMT) No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 10.1556168.2018.19.2.12.pdf: 1067858 bytes, checksum: 67581f069ade551a17298bce97d3a72e (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-05-27T12:46:13Z (GMT). No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 10.1556168.2018.19.2.12.pdf: 1067858 bytes, checksum: 67581f069ade551a17298bce97d3a72e (MD5) Previous issue date: 20184 happlication/pdfenengAkadémiai Kiadó, BudapestCommunity Ecology, 2018, 19: 199–202Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Biodiversity–ecosystem functioning experimentsQuantitative Lotka-Volterra competition theoryCan we make quantitative predictions for relative yield with incomplete knowledge of model parameters?Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaFort, HugoLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/31683/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-844http://localhost:8080/xmlui/bitstream/20.500.12008/31683/2/license_urla0ebbeafb9d2ec7cbb19d7137ebc392cMD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse
spellingShingle Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
Fort, Hugo
Biodiversity–ecosystem functioning experiments
Quantitative Lotka-Volterra competition theory
status_str publishedVersion
title Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
title_full Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
title_fullStr Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
title_full_unstemmed Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
title_short Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
title_sort Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
topic Biodiversity–ecosystem functioning experiments
Quantitative Lotka-Volterra competition theory
url https://hdl.handle.net/20.500.12008/31683